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CyberDefender: an integrated intelligent defense framework for digital-twin-based industrial cyber-physical systems

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Abstract

The rise of digital twin-based operational improvements poses a challenge to protecting industrial cyber-physical systems. It is crucial to safeguard digital twins while disclosing internals, which can create an increased attack surface. However, leveraging digital twins to simulate attacks on physical infrastructure becomes essential for enhancing ICPS cybersecurity resilience. This paper introduces an integrated intelligent defense framework called CyberDefender to study various attacks on digital twin-based ICPS from a four-layer perspective (i.e., digital twin-based industrial cyber-physical systems infrastructure layer, honeynet and software-defined industrial network layer, intelligent security platform layer, and smart industrial application layer). To demonstrate its feasibility, we implemented a proof-of-concept (PoC) solution using open-source tools, including AWS for cloud infrastructure, T-Pot for Honeynet, Mininet for SDN support, ELK tools for data management, and Docker for containerization. This framework utilizes an integrated intelligent approach to enhance intrusion detection and classification capabilities for digital twin-based industrial cyber-physical systems (DT-ICPS). The proposed intrusion detection system (IDS) combines two strategies to improve security. First, we present an innovative approach to identifying essential features using explainable AI and ensemble-based filter feature selection (XAI-EFFS). By using Shapley Additive Explanations (SHAP), we analyze the impact of different variables on predictive outcomes. Secondly, we propose a hybrid GRU-LSTM deep-learning model for detecting and classifying intrusions. We optimize the hyperparameters of the GRU-LSTM model by using a Bayesian optimization algorithm. The proposed method demonstrates excellent performance, outperforming conventional state-of-the-art techniques with an accuracy rate of 98.96%, which is a remarkable improvement. Additionally, it effectively detects zero-day attacks, contributing to digital twin-based ICPS cybersecurity resilience.

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Data availability

As the honeypot dataset was collected and analysed using open-source tools and computer resources available at our institution, it is available upon request from the corresponding author. The public dataset analysed during this study are available at: [Online] Available at: GitHub https://github.com/ngoclesydney/Anomaly-Detection-with-Swat-Dataset, https://drive.google.com/file/d/1cJECqTj7ExPuwCddrCPB5RTnuk5NKvCF/view, all data and software used during this study are cited and included in the references.

Abbreviations

AUC:

Area under curve

BO:

Bayesian optimization

CNN:

Convolutional neural networks

CTF:

Capture-the-flag

DNN:

Deep neural network

DT:

Digital twin

DL:

Deep learning

DDoS:

Distributed denial of service

ELK:

Elasticsearch, logstash, and kibana

ERP:

Enterprise resource planning process

EFFS:

Ensemble-based filter feature selection

ERA:

Enterprise reference architecture

EL:

Ensemble learning

GRU:

Gated recurrent unit

ICPS:

Industrial cyber physical systems

IPS:

Intrusion prevention system

IDS:

Intrusion detection system

ICS:

Industrial control system

LSTM:

Long short-term memory

MES:

Manufacturing execution system

MITM:

Man in-the-middle

ML:

Machine learning

NFV:

Network functions virtualization

NIDS:

Network intrusion detection system

ONOS:

Open network operating system

PLC:

Programmable logic controller

POC:

Proof-of-concept (PoC)

RF:

Random forest

RNN:

Recurrent neural network

TI:

Timing intrusion

ROC:

Receiver operating characteristic

SWaT:

Secure water treatment

SDN:

Software-defined network

SNMP:

Simple network management protocol

SOC:

Security operations centre

XAI:

Explainable artificial intelligence

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Authors and Affiliations

  1. Department of Computational Intelligence, SRM Institute of Science & Technology, Kattankulathur, Chennai, Tamil Nadu, 603 203, India

    S. Krishnaveni

  2. City, University of London, London, UK

    Thomas M. Chen

  3. MIT Square, London, UK

    Mithileysh Sathiyanarayanan

  4. Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, 603 203, India

    B. Amutha

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  1. S. Krishnaveni
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  2. Thomas M. Chen
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  3. Mithileysh Sathiyanarayanan
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  4. B. Amutha
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KS Data collection, evaluate the experiments results, wrote the manuscript and framework methodology design. TS review the manuscript, editing and supervision. MS review the manuscript, editing and supervision, AB Review the manuscript, editing and evaluate the experiments results.

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Correspondence to S. Krishnaveni.

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Krishnaveni, S., Chen, T.M., Sathiyanarayanan, M. et al. CyberDefender: an integrated intelligent defense framework for digital-twin-based industrial cyber-physical systems. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04320-x

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  • DOI: https://doi.org/10.1007/s10586-024-04320-x

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